Overview

Dataset statistics

Number of variables16
Number of observations320
Missing cells92
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.1 KiB
Average record size in memory128.4 B

Variable types

Numeric13
Text3

Alerts

commercial_property is highly overall correlated with crime_rate and 7 other fieldsHigh correlation
crime_rate is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
household_affluency is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
household_size is highly overall correlated with household_affluency and 1 other fieldsHigh correlation
normalised_sales is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
property_value is highly overall correlated with commercial_property and 6 other fieldsHigh correlation
proportion_flats is highly overall correlated with commercial_property and 4 other fieldsHigh correlation
proportion_newbuilds is highly overall correlated with commercial_property and 7 other fieldsHigh correlation
proportion_nonretail is highly overall correlated with commercial_property and 8 other fieldsHigh correlation
public_transport_dist is highly overall correlated with commercial_property and 6 other fieldsHigh correlation
school_proximity is highly overall correlated with normalised_sales and 1 other fieldsHigh correlation
commercial_property has 29 (9.1%) missing valuesMissing
school_proximity has 63 (19.7%) missing valuesMissing
location_id has unique valuesUnique
proportion_flats has 238 (74.4%) zerosZeros
proportion_newbuilds has 23 (7.2%) zerosZeros

Reproduction

Analysis started2024-02-09 10:02:36.591543
Analysis finished2024-02-09 10:02:49.695001
Duration13.1 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

location_id
Real number (ℝ)

UNIQUE 

Distinct320
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean252.3875
Minimum1
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:49.768147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.95
Q1126.5
median251.5
Q3377.25
95-th percentile474.05
Maximum506
Range505
Interquartile range (IQR)250.75

Descriptive statistics

Standard deviation145.60058
Coefficient of variation (CV)0.576893
Kurtosis-1.1937793
Mean252.3875
Median Absolute Deviation (MAD)126
Skewness-0.021020651
Sum80764
Variance21199.53
MonotonicityNot monotonic
2024-02-09T10:02:49.893286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
464 1
 
0.3%
504 1
 
0.3%
23 1
 
0.3%
415 1
 
0.3%
56 1
 
0.3%
195 1
 
0.3%
351 1
 
0.3%
111 1
 
0.3%
328 1
 
0.3%
392 1
 
0.3%
Other values (310) 310
96.9%
ValueCountFrequency (%)
1 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
7 1
0.3%
10 1
0.3%
11 1
0.3%
13 1
0.3%
15 1
0.3%
17 1
0.3%
ValueCountFrequency (%)
506 1
0.3%
504 1
0.3%
503 1
0.3%
501 1
0.3%
500 1
0.3%
498 1
0.3%
494 1
0.3%
491 1
0.3%
489 1
0.3%
488 1
0.3%

crime_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct319
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5963745
Minimum0.0071416
Maximum51.693093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:50.159240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0071416
5-th percentile0.028051685
Q10.0879366
median0.28968115
Q34.0635534
95-th percentile17.156496
Maximum51.693093
Range51.685951
Interquartile range (IQR)3.9756168

Descriptive statistics

Standard deviation7.1763415
Coefficient of variation (CV)1.9954377
Kurtosis13.231406
Mean3.5963745
Median Absolute Deviation (MAD)0.2520239
Skewness3.2555291
Sum1150.8399
Variance51.499878
MonotonicityNot monotonic
2024-02-09T10:02:50.283388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0169613 2
 
0.6%
17.600541 1
 
0.3%
0.2770986 1
 
0.3%
0.7208948 1
 
0.3%
0.0494827 1
 
0.3%
7.3887988 1
 
0.3%
0.1486854 1
 
0.3%
0.2419217 1
 
0.3%
9.3203417 1
 
0.3%
2.614707 1
 
0.3%
Other values (309) 309
96.6%
ValueCountFrequency (%)
0.0071416 1
0.3%
0.0123848 1
0.3%
0.0147013 1
0.3%
0.0148143 1
0.3%
0.015368 1
0.3%
0.0161816 1
0.3%
0.0162607 1
0.3%
0.0169613 2
0.6%
0.0200914 1
0.3%
0.021131 1
0.3%
ValueCountFrequency (%)
51.693093 1
0.3%
43.337534 1
0.3%
42.557947 1
0.3%
32.381054 1
0.3%
29.312878 1
0.3%
28.302093 1
0.3%
28.025921 1
0.3%
27.564994 1
0.3%
25.534723 1
0.3%
24.917743 1
0.3%

proportion_flats
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.673438
Minimum0
Maximum100
Zeros238
Zeros (%)74.4%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:50.389862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile75.25
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation22.579232
Coefficient of variation (CV)2.1154602
Kurtosis4.8618828
Mean10.673438
Median Absolute Deviation (MAD)0
Skewness2.3679581
Sum3415.5
Variance509.82171
MonotonicityNot monotonic
2024-02-09T10:02:50.489942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 238
74.4%
20 13
 
4.1%
25 7
 
2.2%
80 7
 
2.2%
22 7
 
2.2%
12.5 6
 
1.9%
45 5
 
1.6%
55 3
 
0.9%
60 3
 
0.9%
21 3
 
0.9%
Other values (15) 28
 
8.8%
ValueCountFrequency (%)
0 238
74.4%
12.5 6
 
1.9%
17.5 1
 
0.3%
18 1
 
0.3%
20 13
 
4.1%
21 3
 
0.9%
22 7
 
2.2%
25 7
 
2.2%
28 2
 
0.6%
30 3
 
0.9%
ValueCountFrequency (%)
100 1
 
0.3%
95 3
0.9%
90 2
 
0.6%
85 2
 
0.6%
82.5 1
 
0.3%
80 7
2.2%
75 3
0.9%
60 3
0.9%
55 3
0.9%
52.5 1
 
0.3%

proportion_nonretail
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.307906
Minimum0.74
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:50.601191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.74
5-th percentile2.18
Q15.13
median9.9
Q318.1
95-th percentile21.89
Maximum27.74
Range27
Interquartile range (IQR)12.97

Descriptive statistics

Standard deviation7.0326934
Coefficient of variation (CV)0.6219271
Kurtosis-1.2360987
Mean11.307906
Median Absolute Deviation (MAD)6.615
Skewness0.28816142
Sum3618.53
Variance49.458776
MonotonicityNot monotonic
2024-02-09T10:02:50.723128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 84
26.2%
19.58 20
 
6.2%
6.2 13
 
4.1%
8.14 12
 
3.8%
21.89 10
 
3.1%
9.9 9
 
2.8%
3.97 7
 
2.2%
4.05 7
 
2.2%
5.86 7
 
2.2%
8.56 7
 
2.2%
Other values (57) 144
45.0%
ValueCountFrequency (%)
0.74 1
0.3%
1.21 1
0.3%
1.22 1
0.3%
1.25 1
0.3%
1.32 1
0.3%
1.38 1
0.3%
1.47 1
0.3%
1.52 2
0.6%
1.69 1
0.3%
1.76 1
0.3%
ValueCountFrequency (%)
27.74 4
 
1.2%
25.65 6
 
1.9%
21.89 10
 
3.1%
19.58 20
 
6.2%
18.1 84
26.2%
15.04 2
 
0.6%
13.92 3
 
0.9%
13.89 1
 
0.3%
12.83 4
 
1.2%
11.93 4
 
1.2%
Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:50.790300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.059375
Min length2

Characters and Unicode

Total characters659
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 301
94.1%
yes 19
 
5.9%
2024-02-09T10:02:50.952887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 659
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 659
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 301
45.7%
o 301
45.7%
y 19
 
2.9%
e 19
 
2.9%
s 19
 
2.9%

commercial_property
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct76
Distinct (%)26.1%
Missing29
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean16.868557
Minimum1.75
Maximum1009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:51.066581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.75
5-th percentile3.1
Q15.45
median9.4
Q314.05
95-th percentile21
Maximum1009
Range1007.25
Interquartile range (IQR)8.6

Descriptive statistics

Standard deviation73.806051
Coefficient of variation (CV)4.3753625
Kurtosis149.49455
Mean16.868557
Median Absolute Deviation (MAD)4.3
Skewness12.087634
Sum4908.75
Variance5447.3332
MonotonicityNot monotonic
2024-02-09T10:02:51.186528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.15 11
 
3.4%
9.4 11
 
3.4%
13.7 10
 
3.1%
26.05 10
 
3.1%
4.35 10
 
3.1%
12.75 9
 
2.8%
6.95 9
 
2.8%
9.7 8
 
2.5%
4.05 7
 
2.2%
17.5 7
 
2.2%
Other values (66) 199
62.2%
(Missing) 29
 
9.1%
ValueCountFrequency (%)
1.75 1
 
0.3%
1.95 1
 
0.3%
2.55 3
0.9%
2.65 1
 
0.3%
2.7 1
 
0.3%
2.75 1
 
0.3%
2.95 2
0.6%
3 1
 
0.3%
3.05 4
1.2%
3.15 2
0.6%
ValueCountFrequency (%)
1009 1
 
0.3%
767 1
 
0.3%
123 1
 
0.3%
26.05 10
3.1%
21 5
1.6%
19.5 7
2.2%
18.4 3
 
0.9%
18.15 11
3.4%
17.5 7
2.2%
17.15 6
1.9%

household_size
Real number (ℝ)

HIGH CORRELATION 

Distinct298
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2528031
Minimum0.561
Maximum5.725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:51.300150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.561
5-th percentile2.18445
Q12.87975
median3.1975
Q33.59725
95-th percentile4.47095
Maximum5.725
Range5.164
Interquartile range (IQR)0.7175

Descriptive statistics

Standard deviation0.69544191
Coefficient of variation (CV)0.21379772
Kurtosis1.9956769
Mean3.2528031
Median Absolute Deviation (MAD)0.334
Skewness0.18421453
Sum1040.897
Variance0.48363944
MonotonicityNot monotonic
2024-02-09T10:02:51.414934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.229 3
 
0.9%
3.144 2
 
0.6%
3.127 2
 
0.6%
3.03 2
 
0.6%
2.888 2
 
0.6%
3.727 2
 
0.6%
2.966 2
 
0.6%
3.315 2
 
0.6%
3.635 2
 
0.6%
3.417 2
 
0.6%
Other values (288) 299
93.4%
ValueCountFrequency (%)
0.561 1
0.3%
0.863 1
0.3%
1.138 1
0.3%
1.368 1
0.3%
1.519 1
0.3%
1.652 1
0.3%
1.906 1
0.3%
1.926 1
0.3%
1.963 1
0.3%
1.97 1
0.3%
ValueCountFrequency (%)
5.725 1
0.3%
5.375 1
0.3%
5.266 1
0.3%
5.259 1
0.3%
5.247 1
0.3%
5.04 1
0.3%
5.034 1
0.3%
4.929 1
0.3%
4.923 1
0.3%
4.853 1
0.3%

proportion_newbuilds
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct252
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.849062
Minimum0
Maximum94
Zeros23
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:51.527897image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.35
median23.4
Q354.45
95-th percentile82.205
Maximum94
Range94
Interquartile range (IQR)48.1

Descriptive statistics

Standard deviation27.845777
Coefficient of variation (CV)0.87430443
Kurtosis-0.94057168
Mean31.849062
Median Absolute Deviation (MAD)19.95
Skewness0.58915328
Sum10191.7
Variance775.38727
MonotonicityNot monotonic
2024-02-09T10:02:51.645285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
7.2%
4 4
 
1.2%
4.4 3
 
0.9%
1.2 3
 
0.9%
23.5 3
 
0.9%
4.6 3
 
0.9%
78.6 3
 
0.9%
2.7 3
 
0.9%
12 2
 
0.6%
17 2
 
0.6%
Other values (242) 271
84.7%
ValueCountFrequency (%)
0 23
7.2%
0.7 1
 
0.3%
1.1 1
 
0.3%
1.2 3
 
0.9%
1.3 1
 
0.3%
1.5 1
 
0.3%
1.6 2
 
0.6%
1.8 2
 
0.6%
1.9 1
 
0.3%
2 1
 
0.3%
ValueCountFrequency (%)
94 1
0.3%
93.8 1
0.3%
93.5 1
0.3%
93.4 1
0.3%
92.2 2
0.6%
91.6 1
0.3%
91.1 1
0.3%
90.2 1
0.3%
90.1 1
0.3%
87 1
0.3%

public_transport_dist
Real number (ℝ)

HIGH CORRELATION 

Distinct286
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.718765
Minimum1.137
Maximum10.7103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:51.766582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.137
5-th percentile1.50955
Q12.138075
median3.09575
Q35.1167
95-th percentile7.66147
Maximum10.7103
Range9.5733
Interquartile range (IQR)2.978625

Descriptive statistics

Standard deviation1.9847652
Coefficient of variation (CV)0.53371623
Kurtosis0.14082632
Mean3.718765
Median Absolute Deviation (MAD)1.1629
Skewness0.94971955
Sum1190.0048
Variance3.9392931
MonotonicityNot monotonic
2024-02-09T10:02:51.886299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6519 3
 
0.9%
6.8147 3
 
0.9%
5.2873 3
 
0.9%
4.8122 3
 
0.9%
5.4007 3
 
0.9%
6.4798 3
 
0.9%
6.4584 2
 
0.6%
2.4259 2
 
0.6%
6.2196 2
 
0.6%
2.8944 2
 
0.6%
Other values (276) 294
91.9%
ValueCountFrequency (%)
1.137 1
0.3%
1.1691 1
0.3%
1.1742 1
0.3%
1.2024 1
0.3%
1.2852 1
0.3%
1.3216 1
0.3%
1.3325 1
0.3%
1.3449 1
0.3%
1.358 1
0.3%
1.4191 1
0.3%
ValueCountFrequency (%)
10.7103 1
0.3%
9.2229 1
0.3%
9.1876 1
0.3%
9.0892 1
0.3%
8.9067 1
0.3%
8.7921 1
0.3%
8.6966 1
0.3%
8.5353 1
0.3%
8.344 1
0.3%
8.3248 1
0.3%
Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:51.983500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length21.865625
Min length20

Characters and Unicode

Total characters6997
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll transport options
2nd rowAverage transport options
3rd rowMany transport options
4th rowNo transport options
5th rowAverage transport options
ValueCountFrequency (%)
transport 320
33.3%
options 320
33.3%
all 84
 
8.8%
average 72
 
7.5%
few 69
 
7.2%
no 53
 
5.5%
many 42
 
4.4%
2024-02-09T10:02:52.179237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1013
14.5%
t 960
13.7%
r 712
10.2%
n 682
9.7%
640
9.1%
s 640
9.1%
p 640
9.1%
a 434
6.2%
i 320
 
4.6%
e 213
 
3.0%
Other values (9) 743
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6037
86.3%
Space Separator 640
 
9.1%
Uppercase Letter 320
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1013
16.8%
t 960
15.9%
r 712
11.8%
n 682
11.3%
s 640
10.6%
p 640
10.6%
a 434
7.2%
i 320
 
5.3%
e 213
 
3.5%
l 168
 
2.8%
Other values (4) 255
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
A 156
48.8%
F 69
21.6%
N 53
 
16.6%
M 42
 
13.1%
Space Separator
ValueCountFrequency (%)
640
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6357
90.9%
Common 640
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1013
15.9%
t 960
15.1%
r 712
11.2%
n 682
10.7%
s 640
10.1%
p 640
10.1%
a 434
6.8%
i 320
 
5.0%
e 213
 
3.4%
l 168
 
2.6%
Other values (8) 575
9.0%
Common
ValueCountFrequency (%)
640
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6997
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1013
14.5%
t 960
13.7%
r 712
10.2%
n 682
9.7%
640
9.1%
s 640
9.1%
p 640
9.1%
a 434
6.2%
i 320
 
4.6%
e 213
 
3.0%
Other values (9) 743
10.6%

property_value
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.83438
Minimum188
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:52.296311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum188
5-th percentile216
Q1277
median330
Q3666
95-th percentile666
Maximum711
Range523
Interquartile range (IQR)389

Descriptive statistics

Standard deviation170.88897
Coefficient of variation (CV)0.41799072
Kurtosis-1.1841982
Mean408.83438
Median Absolute Deviation (MAD)79.5
Skewness0.63298263
Sum130827
Variance29203.041
MonotonicityNot monotonic
2024-02-09T10:02:52.424978image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 84
26.2%
307 25
 
7.8%
403 20
 
6.2%
437 10
 
3.1%
304 9
 
2.8%
398 9
 
2.8%
296 8
 
2.5%
224 8
 
2.5%
330 7
 
2.2%
264 7
 
2.2%
Other values (48) 133
41.6%
ValueCountFrequency (%)
188 6
1.9%
193 6
1.9%
198 1
 
0.3%
216 4
1.2%
222 5
1.6%
223 3
 
0.9%
224 8
2.5%
226 1
 
0.3%
233 6
1.9%
241 1
 
0.3%
ValueCountFrequency (%)
711 4
 
1.2%
666 84
26.2%
469 1
 
0.3%
437 10
 
3.1%
432 6
 
1.9%
430 2
 
0.6%
422 1
 
0.3%
411 1
 
0.3%
403 20
 
6.2%
402 1
 
0.3%

school_proximity
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)15.6%
Missing63
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean18.589494
Minimum13
Maximum21.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:52.541884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile14.7
Q117.4
median19.1
Q320.2
95-th percentile21
Maximum21.2
Range8.2
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.0755286
Coefficient of variation (CV)0.11165062
Kurtosis-0.12017046
Mean18.589494
Median Absolute Deviation (MAD)1.1
Skewness-0.87250548
Sum4777.5
Variance4.3078189
MonotonicityNot monotonic
2024-02-09T10:02:52.802238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20.2 77
24.1%
14.7 14
 
4.4%
21 13
 
4.1%
18.4 11
 
3.4%
19.1 10
 
3.1%
17.8 10
 
3.1%
18.6 9
 
2.8%
17.4 9
 
2.8%
21.2 9
 
2.8%
16.6 9
 
2.8%
Other values (30) 86
26.9%
(Missing) 63
19.7%
ValueCountFrequency (%)
13 6
1.9%
13.6 1
 
0.3%
14.7 14
4.4%
14.9 1
 
0.3%
15.1 1
 
0.3%
15.2 7
2.2%
15.3 2
 
0.6%
15.6 2
 
0.6%
15.9 1
 
0.3%
16 3
 
0.9%
ValueCountFrequency (%)
21.2 9
 
2.8%
21.1 1
 
0.3%
21 13
 
4.1%
20.9 6
 
1.9%
20.2 77
24.1%
20.1 3
 
0.9%
19.7 4
 
1.2%
19.6 3
 
0.9%
19.2 7
 
2.2%
19.1 10
 
3.1%

competitor_density
Real number (ℝ)

Distinct226
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.65756
Minimum3.5
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:52.911541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile97.889
Q1376.7225
median392.205
Q3396.3525
95-th percentile396.9
Maximum396.9
Range393.4
Interquartile range (IQR)19.63

Descriptive statistics

Standard deviation86.048632
Coefficient of variation (CV)0.23925156
Kurtosis7.9850234
Mean359.65756
Median Absolute Deviation (MAD)4.695
Skewness-2.9883666
Sum115090.42
Variance7404.3671
MonotonicityNot monotonic
2024-02-09T10:02:53.035852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 79
 
24.7%
395.24 3
 
0.9%
393.37 2
 
0.6%
392.8 2
 
0.6%
377.07 2
 
0.6%
388.45 2
 
0.6%
374.71 2
 
0.6%
393.23 2
 
0.6%
395.62 2
 
0.6%
395.11 2
 
0.6%
Other values (216) 222
69.4%
ValueCountFrequency (%)
3.5 1
0.3%
3.65 1
0.3%
7.68 1
0.3%
9.32 1
0.3%
18.82 1
0.3%
22.01 1
0.3%
27.25 1
0.3%
43.06 1
0.3%
48.45 1
0.3%
50.92 1
0.3%
ValueCountFrequency (%)
396.9 79
24.7%
396.42 1
 
0.3%
396.33 1
 
0.3%
396.3 1
 
0.3%
396.28 1
 
0.3%
396.24 1
 
0.3%
396.21 2
 
0.6%
396.14 1
 
0.3%
396.06 1
 
0.3%
395.99 1
 
0.3%

household_affluency
Real number (ℝ)

HIGH CORRELATION 

Distinct298
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1440078
Minimum0.4325
Maximum9.4925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:53.159560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.4325
5-th percentile0.923625
Q11.80375
median2.80875
Q34.091875
95-th percentile6.693125
Maximum9.4925
Range9.06
Interquartile range (IQR)2.288125

Descriptive statistics

Standard deviation1.7740414
Coefficient of variation (CV)0.56426114
Kurtosis0.77003274
Mean3.1440078
Median Absolute Deviation (MAD)1.125
Skewness0.98958754
Sum1006.0825
Variance3.1472231
MonotonicityNot monotonic
2024-02-09T10:02:53.279420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5325 3
 
0.9%
2.6125 2
 
0.6%
2.025 2
 
0.6%
1.9 2
 
0.6%
1.375 2
 
0.6%
1.59 2
 
0.6%
1.1125 2
 
0.6%
1.8475 2
 
0.6%
1.9475 2
 
0.6%
3.3175 2
 
0.6%
Other values (288) 299
93.4%
ValueCountFrequency (%)
0.4325 1
0.3%
0.495 1
0.3%
0.7175 1
0.3%
0.72 1
0.3%
0.735 1
0.3%
0.74 1
0.3%
0.7525 1
0.3%
0.7825 1
0.3%
0.79 2
0.6%
0.815 1
0.3%
ValueCountFrequency (%)
9.4925 1
0.3%
9.245 1
0.3%
8.6925 1
0.3%
8.6025 1
0.3%
7.9975 1
0.3%
7.6575 1
0.3%
7.6475 1
0.3%
7.42 1
0.3%
7.3875 1
0.3%
7.3825 1
0.3%

normalised_sales
Real number (ℝ)

HIGH CORRELATION 

Distinct188
Distinct (%)58.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.016966731
Minimum-1.936974
Maximum2.9684773
Zeros0
Zeros (%)0.0%
Negative187
Negative (%)58.4%
Memory size2.6 KiB
2024-02-09T10:02:53.398062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1.936974
5-th percentile-1.3374188
Q1-0.58524963
median-0.14375902
Q30.24322658
95-th percentile2.1852402
Maximum2.9684773
Range4.9054512
Interquartile range (IQR)0.82847621

Descriptive statistics

Standard deviation0.97856136
Coefficient of variation (CV)-57.675302
Kurtosis1.6704039
Mean-0.016966731
Median Absolute Deviation (MAD)0.4033371
Skewness1.1175046
Sum-5.4293541
Variance0.95758233
MonotonicityNot monotonic
2024-02-09T10:02:53.521655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.96847726 10
 
3.1%
0.2432265774 5
 
1.6%
-0.3781305781 5
 
1.6%
-0.5416456191 5
 
1.6%
0.1233155474 5
 
1.6%
0.03610752556 5
 
1.6%
-0.3672295754 5
 
1.6%
-0.2364175427 5
 
1.6%
-0.1492095208 4
 
1.2%
-0.06200149901 4
 
1.2%
Other values (178) 267
83.4%
ValueCountFrequency (%)
-1.936973968 1
0.3%
-1.871567952 1
0.3%
-1.718953914 1
0.3%
-1.697151908 2
0.6%
-1.675349903 1
0.3%
-1.599042884 1
0.3%
-1.577240878 2
0.6%
-1.566339876 1
0.3%
-1.533636867 1
0.3%
-1.522735865 1
0.3%
ValueCountFrequency (%)
2.96847726 10
3.1%
2.804962219 1
 
0.3%
2.783160213 1
 
0.3%
2.53243715 1
 
0.3%
2.401625118 1
 
0.3%
2.259912082 1
 
0.3%
2.216308071 1
 
0.3%
2.183605063 1
 
0.3%
1.856574981 1
 
0.3%
1.649455929 1
 
0.3%

county
Text

Distinct98
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-02-09T10:02:53.709378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.046875
Min length3

Characters and Unicode

Total characters1295
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)9.4%

Sample

1st rowc_40
2nd rowc_80
3rd rowc_53
4th rowc_65
5th rowc_97
ValueCountFrequency (%)
c_61 10
 
3.1%
c_50 10
 
3.1%
c_60 10
 
3.1%
c_72 9
 
2.8%
c_45 9
 
2.8%
c_68 8
 
2.5%
c_48 8
 
2.5%
c_62 7
 
2.2%
c_39 7
 
2.2%
c_63 7
 
2.2%
Other values (88) 235
73.4%
2024-02-09T10:02:54.013515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 320
24.7%
_ 320
24.7%
6 89
 
6.9%
4 82
 
6.3%
5 80
 
6.2%
7 69
 
5.3%
3 67
 
5.2%
2 64
 
4.9%
1 55
 
4.2%
8 54
 
4.2%
Other values (2) 95
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 655
50.6%
Lowercase Letter 320
24.7%
Connector Punctuation 320
24.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 89
13.6%
4 82
12.5%
5 80
12.2%
7 69
10.5%
3 67
10.2%
2 64
9.8%
1 55
8.4%
8 54
8.2%
9 52
7.9%
0 43
6.6%
Lowercase Letter
ValueCountFrequency (%)
c 320
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 320
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 975
75.3%
Latin 320
 
24.7%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 320
32.8%
6 89
 
9.1%
4 82
 
8.4%
5 80
 
8.2%
7 69
 
7.1%
3 67
 
6.9%
2 64
 
6.6%
1 55
 
5.6%
8 54
 
5.5%
9 52
 
5.3%
Latin
ValueCountFrequency (%)
c 320
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 320
24.7%
_ 320
24.7%
6 89
 
6.9%
4 82
 
6.3%
5 80
 
6.2%
7 69
 
5.3%
3 67
 
5.2%
2 64
 
4.9%
1 55
 
4.2%
8 54
 
4.2%
Other values (2) 95
 
7.3%

Interactions

2024-02-09T10:02:48.373003image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.001266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.993736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.910744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.826733image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.861612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.771748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.662018image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.555539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.584252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.491341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.358384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.474097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.445418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.120077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.066290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.983015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.897096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.933121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.840051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.733246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.759115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.654789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.557676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.434545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.543692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.516381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.192914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.137934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.053053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.967410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.000262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.908588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.800413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.827662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.726745image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.623914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.510530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.612252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.588784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.266667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.210011image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.123523image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.039107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.072760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.977387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.870574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.897271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.796896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.690587image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.587312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.681567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.660081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.340409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.281031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.194344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.108804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.144240image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.046738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.939319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.966219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.867945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.757166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.663830image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.751466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.731513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.412665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.349332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.266885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.303902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.213238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.116435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.008863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.036051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.936771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.825391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.741955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.820715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.798913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.482832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.416949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.335511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.369340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.282797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.181892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.074030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.101631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.004343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.892813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.813992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.888318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.868641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.553525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.484453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.404449image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.437889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.349220image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.250670image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.141517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.170241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.071229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.959246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.887508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.956346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.937431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.624517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.552707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.471659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.504396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.417140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.316398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.208502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.235793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.139894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.024195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.958898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.023257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:49.009128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.698510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.626418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.543620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.574972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.489492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.387635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.278369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.305701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.209879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.092041image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.176159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.094067image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:49.074130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.764189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.693814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.606156image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.637709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.553630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.449710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.339617image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.367060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.274699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.153204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.241997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.158614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:49.154349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.846809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.772893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.688096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.719251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.632073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.528559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.419838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.449554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.353451image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.230400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.325090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.238327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:49.225435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:37.917902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:38.841046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:39.755893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:40.786182image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:41.702089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:42.593219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:43.485662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:44.515052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:45.421296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:46.292299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:47.396553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-09T10:02:48.304147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-09T10:02:54.104400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
commercial_propertycompetitor_densitycrime_ratehousehold_affluencyhousehold_sizelocation_idnormalised_salesproperty_valueproportion_flatsproportion_newbuildsproportion_nonretailpublic_transport_distschool_proximity
commercial_property1.000-0.2770.7790.612-0.3490.013-0.5510.640-0.627-0.7880.756-0.8570.399
competitor_density-0.2771.000-0.328-0.2020.0490.0420.145-0.2870.1570.210-0.2850.226-0.072
crime_rate0.779-0.3281.0000.640-0.3670.059-0.5450.735-0.558-0.6840.716-0.7130.466
household_affluency0.612-0.2020.6401.000-0.650-0.019-0.8620.536-0.482-0.6560.649-0.5800.436
household_size-0.3490.049-0.367-0.6501.000-0.0900.641-0.3180.3930.301-0.4730.329-0.263
location_id0.0130.0420.059-0.019-0.0901.0000.033-0.0110.035-0.0140.0140.0130.033
normalised_sales-0.5510.145-0.545-0.8620.6410.0331.000-0.5460.4420.562-0.5860.456-0.527
property_value0.640-0.2870.7350.536-0.318-0.011-0.5461.000-0.370-0.5350.658-0.5570.474
proportion_flats-0.6270.157-0.558-0.4820.3930.0350.442-0.3701.0000.550-0.6330.611-0.451
proportion_newbuilds-0.7880.210-0.684-0.6560.301-0.0140.562-0.5350.5501.000-0.6790.822-0.427
proportion_nonretail0.756-0.2850.7160.649-0.4730.014-0.5860.658-0.633-0.6791.000-0.7470.500
public_transport_dist-0.8570.226-0.713-0.5800.3290.0130.456-0.5570.6110.822-0.7471.000-0.374
school_proximity0.399-0.0720.4660.436-0.2630.033-0.5270.474-0.451-0.4270.500-0.3741.000

Missing values

2024-02-09T10:02:49.359006image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-09T10:02:49.561826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

location_idcrime_rateproportion_flatsproportion_nonretailnew_storecommercial_propertyhousehold_sizeproportion_newbuildspublic_transport_disttransport_availabilityproperty_valueschool_proximitycompetitor_densityhousehold_affluencynormalised_salescounty
046417.6005410.018.10noNaN2.92629.02.9084All transport options66620.2368.744.5325-0.399933c_40
15040.60355620.03.97no14.854.52010.62.1398Average transport options26413.0388.371.81502.216308c_80
22950.6068100.06.20no7.702.98131.93.6715Many transport options30717.4378.352.91250.166920c_53
31870.01238555.02.25no1.953.45368.17.3073No transport options30015.3394.722.0575-0.083804c_65
41930.016182100.01.32no3.053.81659.58.3248Average transport options25615.1392.900.98750.962693c_97
51600.0686590.011.93no11.153.9769.02.1675No transport options27321.0396.901.41000.123316c_69
6430.25412612.57.87no8.703.3775.76.3467Average transport options31115.2392.525.1125-0.846874c_22
72786.5811310.018.10no9.103.24235.33.4242All transport options66620.2396.902.68500.025207c_54
838717.9221390.018.10no16.452.8964.61.9096All transport options66620.27.686.0975-1.577241c_51
9985.4377070.018.10no18.153.70110.02.5975All transport options66620.2255.234.1050-0.694260c_47
location_idcrime_rateproportion_flatsproportion_nonretailnew_storecommercial_propertyhousehold_sizeproportion_newbuildspublic_transport_disttransport_availabilityproperty_valueschool_proximitycompetitor_densityhousehold_affluencynormalised_salescounty
3102890.1541550.010.59no6.952.89177.73.9454Few transport options27718.6396.902.7175-0.018397c_46
311951.3643850.019.58no12.752.8755.42.4259Average transport options40314.7292.293.6075-0.585250c_31
31236110.5529460.018.10no16.453.3804.41.9682All transport options66620.260.726.0200-1.446429c_50
31321024.9177430.018.10noNaN2.8187.61.8662All transport options66620.2391.455.5275-1.337419c_24
314996.3066090.018.10no18.153.43612.12.3158All transport options66620.2100.194.0550-0.923181c_44
3151060.10246845.03.44noNaN3.95178.56.4798Average transport options39815.2377.681.27501.551347c_68
316240.62157920.03.97no14.854.2068.41.9301Average transport options26413.0387.892.02501.496842c_99
3174730.9070620.08.14noNaN2.45663.43.7965Few transport options30721.0288.992.9225-0.280022c_36
318760.1050790.025.65no11.552.9617.12.0869No transport options188NaN378.094.4825-0.247319c_69
3194010.03445455.03.78no6.703.87471.96.4654Average transport options37017.6387.971.15250.919089c_76